AP DETECTOR: CROWDSOURCING-BASED APPROACH FOR SELF-LOCALIZATION OF WI-FI FTM STATIONS

Abstract. The acquisition of locations of Wi-Fi access points (APs) in urban buildings plays an important role in smart city applications, such as indoor navigation and social media data mining. This paper proposes a crowdsourcing-based approach for self-localization of Wi-Fi APs with the assistance of indoor pedestrian network (AP Detector). The features extracted from local opportunity signals are adopted for floor identification, and the crowdsourced indoor trajectories are segmented and matched with extracted indoor pedestrian network for the further trajectory calibration. In addition, the iteration unscented Kalman filter is applied for the location and bias estimation of local Wi-Fi FTM stations using the constructed Wi-Fi ranging model. The experimental results indicate that the proposed AP Detector can realize accurate location estimation of Wi-Fi APs, which also provides an effective way for autonomous construction of indoor navigation database and hybrid localization.

Angaben zum Objekt

• Standort: Deutsche Nationalbibliothek Frankfurt am Main
• Umfang: Online-Ressource
• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: AP DETECTOR: CROWDSOURCING-BASED APPROACH FOR SELF-LOCALIZATION OF WI-FI FTM STATIONS ; volume:XLVI-3/W1-2022 ; year:2022 ; pages:249-254 ; extent:6 ; The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ; XLVI-3/W1-2022 (2022), 249-254 (gesamt 6)

Klassifikation und Themen

• Klassifikation: Elektrotechnik, Elektronik

Beteiligte, Orts- und Zeitangaben

• Urheber: Yu, Y. ; Shi, W. ; Chen, R. ; Chen, L.

Weitere Informationen

• DOI: 10.5194/isprs-archives-XLVI-3-W1-2022-249-2022
• URN: urn:nbn:de:101:1-2022042805260671878233
• Rechteinformation: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Letzte Aktualisierung: 15.08.2025, 07:24 MESZ